jl tests_website_v2
This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Cmd+Shift+Enter.
# install.packages("testthat")
# install.packages("devtools")
# install.packages("dplyr")
# install.packages("ICC")
# install.packages("MetaUtility")
# install.packages("ggplot2")
library(testthat)
library(EValue)
library(devtools)
library(dplyr)
library(ICC)
library(MetaUtility)
library(ggplot2)
library(boot)
# library(here)Below tests run on 11/19/20 with github code pull on 11/19/20:
# setwd(here())
# source("startup.R")
setwd("~/Box Sync/jlee/Maya/metasens_website/Main site/tests_human_inspection")
# source("helper_testthat.R")
# source("~/Box Sync/jlee/Maya/evalue/EValue/tests/helper_testthat.R")
# source("~/Box Sync/jlee/Maya/evalue/EValue/R/meta-analysis.R")
#
# setwd("~/Box Sync/jlee/Maya/evalue/tests_human_inspection/")test1 gbc_prepped.csv file
d = read.csv("Datasets for website test/gbc_prepped.csv")
confounded_meta(method="calibrated",
q = log(.9),
r = 0.1,
muB = 0,
tail = "below",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.## [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"## Error in data.frame(lo.T, hi.T, SE.T, lo.G, hi.G, SE.G): arguments imply differing number of rows: 1, 0### R output:
# The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.
# [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
# Error in data.frame(lo.T, hi.T, SE.T, lo.G, hi.G, SE.G) :
# arguments imply differing number of rows: 1, 0
### Website output:
knitr::include_graphics("jl_website_test2_1a.png")
sens_plot(method="calibrated",
type = "line",
q = log(0.9),
tail = "below",
Bmin = log(1),
Bmax = log(4),
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 35.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 36.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Some of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot may not be shown for some values of the bias factor. This usually happens at values with a proportion estimate close to 0 or 1. Otherwise, you can try increasing R.
### R output:
# Some of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot may not be shown for some values of the bias factor. This usually happens at values with a proportion estimate close to 0 or 1. Otherwise, you can try increasing R.
# Warning messages:
# 1: Problem with `mutate()` input `..1`.
# ℹ extreme order statistics used as endpoints
# ℹ Input `..1` is `...[]`.
# ℹ The error occurred in row 28.
# 2: In norm.inter(t, adj.alpha) :
# extreme order statistics used as endpoints
# 3: Problem with `mutate()` input `..1`.
# ℹ extreme order statistics used as endpoints
# ℹ Input `..1` is `...[]`.
# ℹ The error occurred in row 35.
# 4: In norm.inter(t, adj.alpha) :
# extreme order statistics used as endpoints
# 5: Problem with `mutate()` input `..1`.
# ℹ extreme order statistics used as endpoints
# ℹ Input `..1` is `...[]`.
# ℹ The error occurred in row 36.
# 6: In norm.inter(t, adj.alpha) :
# extreme order statistics used as endpoints
### Website output:
knitr::include_graphics("jl_website_test2_1b.png")
test2 gbc_prepped.csv file
d = read.csv("Datasets for website test/gbc_prepped.csv")
confounded_meta(method="calibrated",
q = log(.5),
r = 0.5,
muB = 0.5,
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.### R output:
# The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.
# Value Est SE CI.lo CI.hi
# 1 Prop 1.000000 NA NA NA
# 2 Tmin 2.133700 0.02639639 2.086307 2.193844
# 3 Gmin 3.689005 0.05413210 3.591753 3.812209
### Website output:
knitr::include_graphics("jl_website_test2_2a.png")
sens_plot(method="calibrated",
type = "line",
q = log(.5),
tail = "above",
Bmin = log(1),
Bmax = log(6),
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 2.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 3.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 4.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 5.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 6.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 8.## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints## Some of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot may not be shown for some values of the bias factor. This usually happens at values with a proportion estimate close to 0 or 1. Otherwise, you can try increasing R.
### R output:
# [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
# Some of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot may not be shown for some values of the bias factor. This usually happens at values with a proportion estimate close to 0 or 1. Otherwise, you can try increasing R.
# Warning messages:
# 1: Problem with `mutate()` input `..1`.
# ℹ extreme order statistics used as endpoints
# ℹ Input `..1` is `...[]`.
# ℹ The error occurred in row 2.
# 2: In norm.inter(t, adj.alpha) :
# extreme order statistics used as endpoints
# 3: Problem with `mutate()` input `..1`.
# ℹ extreme order statistics used as endpoints
# ℹ Input `..1` is `...[]`.
# ℹ The error occurred in row 3.
# 4: In norm.inter(t, adj.alpha) :
# extreme order statistics used as endpoints
# 5: Problem with `mutate()` input `..1`.
# ℹ extreme order statistics used as endpoints
# ℹ Input `..1` is `...[]`.
# ℹ The error occurred in row 4.
# 6: In norm.inter(t, adj.alpha) :
# extreme order statistics used as endpoints
# 7: Problem with `mutate()` input `..1`.
# ℹ extreme order statistics used as endpoints
# ℹ Input `..1` is `...[]`.
# ℹ The error occurred in row 5.
# 8: In norm.inter(t, adj.alpha) :
# extreme order statistics used as endpoints
### Website output:
knitr::include_graphics("jl_website_test2_2b.png")
test3 gbc_prepped.csv file
d = read.csv("Datasets for website test/gbc_prepped.csv")
## get error for column name
confounded_meta(method="calibrated",
q = log(.5),
r = 0.5,
muB = 0.5,
tail = "above",
yi.name = "yi",
vi.name = "vyi",
dat = d,
R = 2000)## Error in Phat_causal(q = q, B = muB, tail = tail, muB.toward.null = muB.toward.null, : dat does not contain a column named vi.name### R output:
# Error in Phat_causal(q = q, B = muB, tail = tail, dat = dat, yi.name = yi.name, :
# dat does not contain a column named vi.name
### Website output:
knitr::include_graphics("jl_website_test2_3.png")
test4 flegal_prepped.csv file
d = read.csv("Datasets for website test/flegal_prepped.csv")
## on log-RR scale:
# log(.5)
confounded_meta(method= "calibrated",
q = -0.6931472,
r = 0.5,
muB = 0.5,
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.### R output:
# The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.
# Value Est SE CI.lo CI.hi
# 1 Prop 1.000000 NA NA NA
# 2 Tmin 1.834157 0.02938043 1.792858 1.911209
# 3 Gmin 3.071078 0.06101954 2.985118 3.230871
### Website output:
knitr::include_graphics("jl_website_test2_4a.png")
sens_plot(method= "calibrated",
type = "line",
q = -0.6931472,
tail = "above",
Bmin = 1,
Bmax = 4,
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"## None of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot is omitted. You can try increasing R.
### R output:
# [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
# None of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot is omitted. You can try increasing R.
### Website output:
knitr::include_graphics("jl_website_test2_4b.png")
test5 flegal_prepped.csv file
d = read.csv("Datasets for website test/flegal_prepped.csv")
## extreme R?
confounded_meta(method="calibrated",
q = log(.5),
r = 0.1,
muB = .5,
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 10000)## The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.### R output:
# The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.
# Value Est SE CI.lo CI.hi
# 1 Prop 1.000000 NA NA NA
# 2 Tmin 2.177725 0.08646707 2.066321 2.353844
# 3 Gmin 3.779212 0.17689797 3.550693 4.138987
### Website output:
knitr::include_graphics("jl_website_test2_5.png")
test6 flegal_prepped.csv file
d = read.csv("Datasets for website test/flegal_prepped.csv")
confounded_meta(method="calibrated",
q = log(1.2),
r = 1.0,
muB = 0,
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)## The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.## [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"## Error in data.frame(lo.T, hi.T, SE.T, lo.G, hi.G, SE.G): arguments imply differing number of rows: 1, 0### R output:
# The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.
# [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
# Error in data.frame(lo.T, hi.T, SE.T, lo.G, hi.G, SE.G) :
# arguments imply differing number of rows: 1, 0
### Website output:
knitr::include_graphics("jl_website_test2_6.png")
test7 data_calib_test_1-1.csv file
d = read.csv("Datasets for website test/data_calib_test_1-1.csv")
## all 0
confounded_meta(method="calibrated",
q = log(0),
r = 0,
muB = 0,
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 0)## The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.## [1] "All values of t are equal to NaN \n Cannot calculate confidence intervals"## The confidence interval and/or standard error for Tmin and Gmin were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.### R output:
# The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.
# [1] "All values of t are equal to NaN \n Cannot calculate confidence intervals"
# The confidence interval and/or standard error for Tmin and Gmin were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.
# Value Est SE CI.lo CI.hi
# 1 Prop 1 NA NA NA
# 2 Tmin Inf NA NA NA
# 3 Gmin NaN NA NA NA
### Website output:
knitr::include_graphics("jl_website_test2_7a.png")
sens_plot(method="calibrated",
type = "line",
q = log(0),
tail = "above",
Bmin = log(0),
Bmax = log(0),
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 0)## Error in seq.default(Bmin, Bmax, 0.01): 'from' must be a finite number### R output:
# Error in seq.default(Bmin, Bmax, 0.01) : 'from' must be a finite number
### Website output:
knitr::include_graphics("jl_website_test2_7b.png")
test8
d = read.csv("Datasets for website test/data_calib_test_1-1.csv")
## parametric method test
confounded_meta(method="parametric",
q=log(1.1),
r=0.2,
tail="above",
muB=log(1.2),
sigB=sqrt(0.35*0.1),
yr=log(1.2),
vyr=0.01,
t2=0.1,
vt2=0.01)### R output:
# Value Est SE CI.lo CI.hi
# 1 Prop 0.3542627 0.1809323 0.000000 0.7088834
# 2 Tmin 1.4235523 0.2369612 1.000000 1.8879878
# 3 Gmin 2.2000501 0.5187985 1.183224 3.2168766
### Website output:
knitr::include_graphics("jl_website_test2_8a.png")
sens_plot(method = "parametric",
type="line",
q=log(1.1),
yr=log(1.2),
vyr=0.01,
t2=0.1,
vt2=0.01,
Bmin=log(1),
Bmax=log(4),
sigB=sqrt(0.35*0.1),
tail="above" )## Warning in sens_plot(method = "parametric", type = "line", q = log(1.1), :
## Calculating parametric confidence intervals in the plot. For values of Phat
## that are less than 0.15 or greater than 0.85, these confidence intervals may not
## perform well.
### R output:
# Warning message:
# In sens_plot(method = "parametric", type = "line", q = log(1.1), :
# Calculating parametric confidence intervals in the plot. For values of Phat that are less than 0.15 or greater than 0.85, these confidence intervals may not perform well.
### Website output:
knitr::include_graphics("jl_website_test2_8b.png")
test9
## parametric method test
## see what errors if all 0
confounded_meta(method="parametric",
q=log(0),
r=0,
tail="above",
muB=log(0),
sigB=0,
yr=log(0),
vyr=0,
t2=0,
vt2=0)## Error in confounded_meta(method = "parametric", q = log(0), r = 0, tail = "above", : Must have t2 > sigB^2### R output:
# Error in confounded_meta(method = "parametric", q = log(0), r = 0, tail = "above", :
# Must have t2 > sigB^2
### Website output:
knitr::include_graphics("jl_website_test2_9.png")
test10
## parametric method test
confounded_meta(method="parametric",
q=log(.5),
r=0.75,
tail="below",
muB=log(1.5),
sigB=sqrt(0.5*0.25),
yr=log(1.5),
vyr=0.5,
t2=0.25,
vt2=0.5)## Warning in confounded_meta(method = "parametric", q = log(0.5), r = 0.75, : Prop
## is close to 0 or 1. We recommend choosing method = "calibrated" or alternatively
## using bias-corrected and accelerated bootstrapping to estimate all inference in
## this case.## Warning in sqrt(Tmin^2 - Tmin): NaNs produced### R output:
# Value Est SE CI.lo CI.hi
# 1 Prop 0.02496774 0.3441508 0 0.6994909
# 2 Tmin 0.23791135 1.8262687 1 3.8173322
# 3 Gmin NaN NaN NaN NaN
# Warning messages:
# 1: In confounded_meta(method = "parametric", q = log(0.5), r = 0.75, :
# Prop is close to 0 or 1. We recommend choosing method = "calibrated" or alternatively using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
# 2: In sqrt(Tmin^2 - Tmin) : NaNs produced
### Website output:
knitr::include_graphics("jl_website_test2_10a.png")
sens_plot(method = "parametric",
type="line",
q=log(.5),
yr=log(1.5),
vyr=0.5,
t2=0.25,
vt2=sqrt(0.5*(0.25)),
Bmin=log(1),
Bmax=log(4),
sigB=sqrt(0.5*0.25),
tail="below" )## Warning in sens_plot(method = "parametric", type = "line", q = log(0.5), :
## Calculating parametric confidence intervals in the plot. For values of Phat
## that are less than 0.15 or greater than 0.85, these confidence intervals may not
## perform well.
### R output:
# Warning message:
# In sens_plot(method = "parametric", type = "line", q = log(0.5), :
# Calculating parametric confidence intervals in the plot. For values of Phat that are less than 0.15 or greater than 0.85, these confidence intervals may not perform well.
### Website output:
knitr::include_graphics("jl_website_test2_10b.png")
test11
## on log-RR scale
# log(1.2)
## parametric method test
confounded_meta(method="parametric",
q=0.1823216,
r=0.2,
tail="below",
muB=.2,
sigB=sqrt(0.15*0.25),
yr=.4,
vyr=0.05,
t2=0.25,
vt2=0.05)### R output:
# Value Est SE CI.lo CI.hi
# 1 Prop 0.4847044 0.1935404 0.1053722 0.8640366
# 2 Tmin 1.2252345 0.5534300 1.0000000 2.3099373
# 3 Gmin 1.7505582 1.3174664 1.0000000 4.3327449
### Website output:
knitr::include_graphics("jl_website_test2_11a.png")
sens_plot(method = "parametric",
type="line",
q=0.1823216,
yr=.4,
vyr=0.05,
t2=0.25,
vt2=0.05,
Bmin=1,
Bmax=6,
sigB=sqrt(0.15*0.25),
tail="below" )## Warning in sens_plot(method = "parametric", type = "line", q = 0.1823216, :
## Calculating parametric confidence intervals in the plot. For values of Phat
## that are less than 0.15 or greater than 0.85, these confidence intervals may not
## perform well.
### R output:
# Warning message:
# In sens_plot(method = "parametric", type = "line", q = 0.1823216, :
# Calculating parametric confidence intervals in the plot. For values of Phat that are less than 0.15 or greater than 0.85, these confidence intervals may not perform well.
### Website output:
knitr::include_graphics("jl_website_test2_11b.png")
test12 kodama_prepped.csv
# MM did this one
d = read.csv("Datasets for website test/kodama_prepped.csv")
confounded_meta(method="calibrated",
q=log(1.5),
r=0.3,
tail="below",
muB=log(1.5),
dat = d,
yi.name = "yi",
vi.name = "vi")## The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.### R output:
# The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing R.
# Value Est SE CI.lo CI.hi
# 1 Prop 0.937500 NA NA NA
# 2 Tmin 1.003351 0.0258029 1 1.131446
# 3 Gmin 1.061336 0.1212162 1 1.517092